






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				lowerCAmelCase_	(		__snake_case			):
				_UpperCamelCase					:       Any							       = ["""image_processor""", """tokenizer"""]
				_UpperCamelCase					:       List[str]							       = """LayoutLMv3ImageProcessor"""
				_UpperCamelCase					:       Dict							       = ("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")


				def __init__(					self ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						**_lowerCAmelCase      ):
											_lowercase							:   Tuple 							=   None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.' ,						__SCREAMING_SNAKE_CASE ,						)
																		_lowercase							:   List[Any] 							=   kwargs.pop('feature_extractor'      )

											_lowercase							:   str 							=   image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('You need to specify an `image_processor`.'      )
											if tokenizer is None:
																		raise ValueError('You need to specify a `tokenizer`.'      )

											super().__init__(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE      )


				def __call__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = True ,						_lowerCAmelCase = False ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = 0 ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = None ,						_lowerCAmelCase = False ,						_lowerCAmelCase = False ,						_lowerCAmelCase = False ,						_lowerCAmelCase = False ,						_lowerCAmelCase = True ,						_lowerCAmelCase = None ,						**_lowerCAmelCase ,						):
											# verify input
											if self.image_processor.apply_ocr and (boxes is not None):
																		raise ValueError(
																		    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'      )

											if self.image_processor.apply_ocr and (word_labels is not None):
																		raise ValueError(
																		    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'      )

											# first, apply the image processor
											_lowercase							:   Optional[int] 							=   self.image_processor(images=__SCREAMING_SNAKE_CASE ,						return_tensors=__SCREAMING_SNAKE_CASE      )

											# second, apply the tokenizer
											if text is not None and self.image_processor.apply_ocr and text_pair is None:
																		if isinstance(__SCREAMING_SNAKE_CASE ,						__SCREAMING_SNAKE_CASE      ):
																									_lowercase							:   Union[str, Any] 							=   [text]  # add batch dimension (as the image processor always adds a batch dimension)
																		_lowercase							:   Union[str, Any] 							=   features['words']

											_lowercase							:   str 							=   self.tokenizer(
											    text=text if text is not None else features['words'] ,						text_pair=text_pair if text_pair is not None else None ,						boxes=boxes if boxes is not None else features['boxes'] ,						word_labels=__SCREAMING_SNAKE_CASE ,						add_special_tokens=__SCREAMING_SNAKE_CASE ,						padding=__SCREAMING_SNAKE_CASE ,						truncation=__SCREAMING_SNAKE_CASE ,						max_length=__SCREAMING_SNAKE_CASE ,						stride=__SCREAMING_SNAKE_CASE ,						pad_to_multiple_of=__SCREAMING_SNAKE_CASE ,						return_token_type_ids=__SCREAMING_SNAKE_CASE ,						return_attention_mask=__SCREAMING_SNAKE_CASE ,						return_overflowing_tokens=__SCREAMING_SNAKE_CASE ,						return_special_tokens_mask=__SCREAMING_SNAKE_CASE ,						return_offsets_mapping=__SCREAMING_SNAKE_CASE ,						return_length=__SCREAMING_SNAKE_CASE ,						verbose=__SCREAMING_SNAKE_CASE ,						return_tensors=__SCREAMING_SNAKE_CASE ,						**__SCREAMING_SNAKE_CASE ,						)

											# add pixel values
											_lowercase							:   Optional[int] 							=   features.pop('pixel_values'      )
											if return_overflowing_tokens is True:
																		_lowercase							:   str 							=   self.get_overflowing_images(__SCREAMING_SNAKE_CASE ,						encoded_inputs['overflow_to_sample_mapping']      )
											_lowercase							:   Tuple 							=   images

											return encoded_inputs


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
											_lowercase							:   Optional[Any] 							=   []
											for sample_idx in overflow_to_sample_mapping:
																		images_with_overflow.append(images[sample_idx]      )

											if len(__SCREAMING_SNAKE_CASE      ) != len(__SCREAMING_SNAKE_CASE      ):
																		raise ValueError(
																		    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
																		    F""" {len(__SCREAMING_SNAKE_CASE      )} and {len(__SCREAMING_SNAKE_CASE      )}"""      )

											return images_with_overflow


				def 						__a					(					self ,						*_lowerCAmelCase ,						**_lowerCAmelCase      ):
											return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE ,						**__SCREAMING_SNAKE_CASE      )


				def 						__a					(					self ,						*_lowerCAmelCase ,						**_lowerCAmelCase      ):
											return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE ,						**__SCREAMING_SNAKE_CASE      )


				@property
				def 						__a					(					self      ):
											return ["input_ids", "bbox", "attention_mask", "pixel_values"]


				@property
				def 						__a					(					self      ):
											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' ,						__SCREAMING_SNAKE_CASE ,						)
											return self.image_processor_class


				@property
				def 						__a					(					self      ):
											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,						__SCREAMING_SNAKE_CASE ,						)
											return self.image_processor












"""simple docstring"""





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					DebertaTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					True
    SCREAMING_SNAKE_CASE		:	Any		=					DebertaTokenizerFast






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Dict:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      lowerCamelCase_       	=		[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '[UNK]',
      ]
      lowerCamelCase_       	=		dict(zip(__SCREAMING_SNAKE_CASE			,		range(len(__SCREAMING_SNAKE_CASE   )   )   )   )
      lowerCamelCase_       	=		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      lowerCamelCase_       	=		{'unk_token': '[UNK]'}

      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file']   )
      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['merges_file']   )
      with open(self.vocab_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write(json.dumps(__SCREAMING_SNAKE_CASE   ) + '\n'   )
      with open(self.merges_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write('\n'.join(__SCREAMING_SNAKE_CASE   )   )






    def   UpperCamelCase			(			self	:				Tuple			,		**__SCREAMING_SNAKE_CASE	:				Dict   )	->						List[Any]:
      kwargs.update(self.special_tokens_map   )
      return self.tokenizer_class.from_pretrained(self.tmpdirname			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Dict:
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		'lower newer'
      return input_text, output_text






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
      lowerCamelCase_       	=		tokenizer.tokenize(__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokens + [tokenizer.unk_token]
      lowerCamelCase_       	=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		tokenizer('Hello'			,		'World'   )
      lowerCamelCase_       	=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
      self.assertListEqual(tokd['token_type_ids']			,		__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.tokenizer_class.from_pretrained('microsoft/deberta-base'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		'multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      lowerCamelCase_       	=		[self.tokenizer_class]
      if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class   )

      for tokenizer_class in tokenizer_classes:
        lowerCamelCase_       	=		tokenizer_class.from_pretrained('microsoft/deberta-base'   )

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        lowerCamelCase_       	=		tokenizer(__SCREAMING_SNAKE_CASE			,		padding=__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		[tokenizer.decode(__SCREAMING_SNAKE_CASE			,		skip_special_tokens=__SCREAMING_SNAKE_CASE   ) for seq in encoding['input_ids']]

        # fmt: off
        lowerCamelCase_       	=		{
            'input_ids': [
                [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
            ],
            'token_type_ids': [
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
            ],
            'attention_mask': [
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
            ]
        }
        # fmt: on

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        self.assertDictEqual(encoding.data			,		__SCREAMING_SNAKE_CASE   )

        for expected, decoded in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          self.assertEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




